ArticleBMC cancer2019
Identifying primary site of lung-limited Cancer of unknown primary based on relative gene expression orderings.
Article in BMC cancer, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- New techniques to identify the tissue of origin for cancer of unknown primary in the era of precision medicine: progress and challenges.Briefings in bioinformatics · 2024Pooled it
- OncoTrace-TOO: Interpretable Machine Learning Framework for Cancer Tissue-of-Origin Identification Using Transcriptomic Signatures.Cancer reports (Hoboken, N.J.) · 2025Article
- Construct of qualitative diagnostic biomarkers specific for glioma by pairing serum microRNAs.BMC genomics · 2023Article
- The diagnosis and treatment for a patient with cancer of unknown primary: A case report.Frontiers in genetics · 2023Article
- Article
- A Qualitative Transcriptional Signature for Predicting CpG Island Methylator Phenotype Status of the Right-Sided Colon Cancer.Frontiers in genetics · 2020Article
- A Deep Learning Framework to Predict Tumor Tissue-of-Origin Based on Copy Number Alteration.Frontiers in bioengineering and biotechnology · 2020Article
- The Treatment for a Patient with Cancer of Unknown Primary: A Case Report.Dose-response : a publication of International Hormesis SocietyArticle
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundPrecise diagnosis of the tissue origin for metastatic cancer of unknown primary (CUP) is essential for deciding the treatment scheme to improve patients' prognoses, since the treatment for the metastases is the same as their primary counterparts. The purpose of this study is to identify a robust gene signature that can predict the origin for CUPs.
methodsThe within-sample relative gene expression orderings (REOs) of gene pairs within individual samples, which are insensitive to experimental batch effects and data normalizations, were exploited for identifying the prediction signature.
resultsUsing gene expression profiles of the lung-limited metastatic colorectal cancer (LmCRC), we firstly showed that the within-sample REOs in lung metastases of colorectal cancer (CRC) samples were concordant with the REOs in primary CRC samples rather than with the REOs in primary lung cancer. Based on this phenomenon, we selected five gene pairs with consistent REOs in 498 primary CRC and reversely consistent REOs in 509 lung cancer samples, which were used as a signature for predicting primary sites of metastatic CRC based on the majority voting rule. Applying the signature to 654 primary CRC and 204 primary lung cancer samples collected from multiple datasets, the prediction accuracy reached 99.36%. This signature was also applied to 24 LmCRC samples collected from three datasets produced by different laboratories and the accuracy reached 100%, suggesting that the within-sample REOs in the primary site could reveal the original tissue of metastatic cancers.
conclusionsThe result demonstrated that the signature based on within-sample REOs of five gene pairs could exactly and robustly identify the primary sites of CUPs.
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